"""simple docstring"""


import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


a				=['\nclass', '\ndef', '\n#', '\n@', '\nprint', '\nif']






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase=None      ,					_lowerCamelCase=1 ):
										lowerCamelCase__   =tokenizer
										lowerCamelCase__   =dataset
										lowerCamelCase__   =len(_lowerCamelCase ) if n_tasks is None else n_tasks
										lowerCamelCase__   =n_copies






				def __iter__( self ):
										lowerCamelCase__   =[]
										for task in range(self.n_tasks ):
																# without strip, the model generate commented codes ...
																prompts.append(self.tokenizer.eos_token + self.dataset[task]["prompt"].strip() )
										lowerCamelCase__   =self.tokenizer(_lowerCamelCase      ,					padding=_lowerCamelCase      ,					return_tensors="pt" )
										for task in range(self.n_tasks ):
																for _ in range(self.n_copies ):
																						yield {
																						    "ids": outputs.input_ids[task],
																						    "task_id": task,
																						    "input_len": outputs.attention_mask[task].sum(),
																						}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):

				def __init__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase ):
										lowerCamelCase__   =start_length
										lowerCamelCase__   =eof_strings
										lowerCamelCase__   =tokenizer






				def __call__( self      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					**_lowerCamelCase ):
										lowerCamelCase__   =self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
										lowerCamelCase__   =[]
										for decoded_generation in decoded_generations:
																done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
										return all(_lowerCamelCase )





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =re.split("(%s)" % "|".join(__lowerCAmelCase							)  ,       __lowerCAmelCase							)
						# last string should be ""
						return "".join(string_list[:-2]							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase=20  ,       **__lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =defaultdict(__lowerCAmelCase							)  # dict of list of generated tokens
						for step, batch in tqdm(enumerate(__lowerCAmelCase							)							):
												with torch.no_grad():
																		lowerCamelCase__   =batch["ids"].shape[-1]
																		lowerCamelCase__   =accelerator.unwrap_model(__lowerCAmelCase							).generate(
																		    input_ids=batch["ids"][:, : batch["input_len"]]  ,       num_return_sequences=__lowerCAmelCase  ,       **__lowerCAmelCase							)
																		# each task is generated batch_size times
																		lowerCamelCase__   =batch["task_id"].repeat(__lowerCAmelCase							)
																		lowerCamelCase__   =accelerator.pad_across_processes(
																		    __lowerCAmelCase  ,       dim=1  ,       pad_index=tokenizer.pad_token_id							)

																		lowerCamelCase__   , lowerCamelCase__   =accelerator.gather((generated_tokens, generated_tasks)							)
																		lowerCamelCase__   =generated_tokens.cpu().numpy()
																		lowerCamelCase__   =generated_tasks.cpu().numpy()

																		for task, generated_tokens in zip(__lowerCAmelCase  ,       __lowerCAmelCase							):
																								gen_token_dict[task].append(__lowerCAmelCase							)

						lowerCamelCase__   =[[] for _ in range(__lowerCAmelCase							)]
						for task, generated_tokens in gen_token_dict.items():
												for s in generated_tokens:
																		lowerCamelCase__   =tokenizer.decode(__lowerCAmelCase  ,       skip_special_tokens=__lowerCAmelCase  ,       clean_up_tokenization_spaces=__lowerCAmelCase							)
																		code_gens[task].append(remove_last_block(__lowerCAmelCase							)							)
						return code_gens





def      lowerCamelCase_					(   )				->   List[str]:




						'''simple docstring'''



						lowerCamelCase__   =HfArgumentParser(__lowerCAmelCase							)
						lowerCamelCase__   =parser.parse_args()

						transformers.logging.set_verbosity_error()
						# enables code execution in code_eval metric
						lowerCamelCase__   =args.HF_ALLOW_CODE_EVAL
						# make sure tokenizer plays nice with multiprocessing
						lowerCamelCase__   ="false"

						if args.num_workers is None:
												lowerCamelCase__   =multiprocessing.cpu_count()

						# Use dataset load to feed to accelerate
						lowerCamelCase__   =Accelerator()
						set_seed(args.seed  ,       device_specific=__lowerCAmelCase							)

						# Load model and tokenizer
						lowerCamelCase__   =AutoTokenizer.from_pretrained(args.model_ckpt							)
						lowerCamelCase__   =tokenizer.eos_token
						lowerCamelCase__   =AutoModelForCausalLM.from_pretrained(args.model_ckpt							)

						# Generation settings
						lowerCamelCase__   ={
						    "do_sample": args.do_sample,
						    "temperature": args.temperature,
						    "max_new_tokens": args.max_new_tokens,
						    "top_p": args.top_p,
						    "top_k": args.top_k,
						    "stopping_criteria": StoppingCriteriaList([EndOfFunctionCriteria(0  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)]							),
						}

						# Load evaluation dataset and metric
						lowerCamelCase__   =load_dataset("openai_humaneval"							)
						lowerCamelCase__   =load_metric("code_eval"							)

						lowerCamelCase__   =args.num_tasks if args.num_tasks is not None else len(human_eval["test"]							)
						lowerCamelCase__   =args.n_samples // args.batch_size

						lowerCamelCase__   =TokenizedDataset(__lowerCAmelCase  ,       human_eval["test"]  ,       n_copies=__lowerCAmelCase  ,       n_tasks=__lowerCAmelCase							)
						# do not confuse args.batch_size, which is actually the num_return_sequences
						lowerCamelCase__   =DataLoader(__lowerCAmelCase  ,       batch_size=1							)

						# Run a quick test to see if code evaluation is enabled
						try:
												lowerCamelCase__   =code_eval_metric.compute(references=[""]  ,       predictions=[[""]]							)
						except ValueError as exception:
												print(
												    "Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL=\"1\"`"
												    " flag to enable code evaluation."							)
												raise exception

						lowerCamelCase__   , lowerCamelCase__   =accelerator.prepare(__lowerCAmelCase  ,       __lowerCAmelCase							)

						lowerCamelCase__   =complete_code(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       n_tasks=__lowerCAmelCase  ,       batch_size=args.batch_size  ,       **__lowerCAmelCase  ,       )

						if accelerator.is_main_process:
												lowerCamelCase__   =[]

												for task in tqdm(range(__lowerCAmelCase							)							):
																		lowerCamelCase__   =human_eval["test"][task]["test"]
																		lowerCamelCase__   =F'''check({human_eval['test'][task]['entry_point']})'''
																		references.append("\n" + test_func + "\n" + entry_point							)

												# Evaluate completions with "code_eval" metric
												lowerCamelCase__   , lowerCamelCase__   =code_eval_metric.compute(
												    references=__lowerCAmelCase  ,       predictions=__lowerCAmelCase  ,       num_workers=args.num_workers							)
												print(F'''Results: {pass_at_k}'''							)

												# Save results to json file
												with open(args.output_file  ,       "w"							) as fp:
																		json.dump(__lowerCAmelCase  ,       __lowerCAmelCase							)


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
	main()



"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square(__lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												# BASE CASE
												if row >= rows or col >= cols:
																		return 0

												lowerCamelCase__   =update_area_of_max_square(__lowerCAmelCase  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       col + 1							)
												lowerCamelCase__   =update_area_of_max_square(row + 1  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						update_area_of_max_square(0  ,       0							)
						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						def update_area_of_max_square_using_dp_array(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							) -> int:
												if row >= rows or col >= cols:
																		return 0
												if dp_array[row][col] != -1:
																		return dp_array[row][col]

												lowerCamelCase__   =update_area_of_max_square_using_dp_array(__lowerCAmelCase  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       col + 1  ,       __lowerCAmelCase							)
												lowerCamelCase__   =update_area_of_max_square_using_dp_array(row + 1  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)

												if mat[row][col]:
																		lowerCamelCase__   =1 + min([right, diagonal, down]							)
																		lowerCamelCase__   =max(largest_square_area[0]  ,       __lowerCAmelCase							)
																		lowerCamelCase__   =sub_problem_sol
																		return sub_problem_sol
												else:
																		return 0

						lowerCamelCase__   =[0]
						lowerCamelCase__   =[[-1] * cols for _ in range(__lowerCAmelCase							)]
						update_area_of_max_square_using_dp_array(0  ,       0  ,       __lowerCAmelCase							)

						return largest_square_area[0]





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[[0] * (cols + 1) for _ in range(rows + 1							)]
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =dp_array[row][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col + 1]
																		lowerCamelCase__   =dp_array[row + 1][col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(dp_array[row][col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0

						return largest_square_area





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =[0] * (cols + 1)
						lowerCamelCase__   =0
						for row in range(rows - 1  ,       -1  ,       -1							):
												for col in range(cols - 1  ,       -1  ,       -1							):
																		lowerCamelCase__   =current_row[col + 1]
																		lowerCamelCase__   =next_row[col + 1]
																		lowerCamelCase__   =next_row[col]

																		if mat[row][col] == 1:
																								lowerCamelCase__   =1 + min(__lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
																								lowerCamelCase__   =max(current_row[col]  ,       __lowerCAmelCase							)
																		else:
																								lowerCamelCase__   =0
												lowerCamelCase__   =current_row

						return largest_square_area


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))


